We’ve all heard the hype around artificial intelligence (AI). It’s everywhere—helping us pick movies, manage our calendars and even drive our cars. But here’s the thing: while the algorithms and tech behind AI get all the glory, what really makes these systems smart is data. And not just any data—quality data. This important topic came to us from Fast Company in their article, “Can we all focus on the data behind the magic?“
There’s an old saying in the tech world: garbage in, garbage out. Basically, if you feed bad data into an AI model, it’s going to produce bad results. If your data is messy, incomplete or biased, your AI won’t stand a chance. To build AI that works, you need data that’s clean, accurate and up-to-date. High-quality data helps AI systems learn better, make smarter predictions and, ultimately, give you better results.
Sure, having a lot of data is great—it gives your AI more to work with—but quality trumps quantity every time. If you’re drowning in data but it’s full of errors, inconsistencies or missing info, it’s not going to help much. In fact, too much bad data can confuse an AI model, making it less effective.
The real challenge is that most organizations have little knowledge on how AI systems make decisions. Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms.
Data Harmony is a fully customizable suite of software products designed to maximize precise and efficient information management and retrieval. Our suite includes tools for taxonomy and thesaurus construction, machine aided indexing, database management, information retrieval, and explainable AI.
Melody K. Smith
| Data Harmony is an award-winning semantic suite that leverages explainable AI. |
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Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.
